Overview

Dataset statistics

Number of variables14
Number of observations422
Missing cells13
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory46.3 KiB
Average record size in memory112.3 B

Variable types

Unsupported1
Numeric12
Categorical1

Warnings

Year is highly correlated with Murder and 10 other fieldsHigh correlation
Murder is highly correlated with Year and 10 other fieldsHigh correlation
Assault on women is highly correlated with Year and 10 other fieldsHigh correlation
Kidnapping and Abduction is highly correlated with Year and 10 other fieldsHigh correlation
Dacoity is highly correlated with Year and 10 other fieldsHigh correlation
Robbery is highly correlated with Year and 10 other fieldsHigh correlation
Arson is highly correlated with Year and 10 other fieldsHigh correlation
Hurt is highly correlated with Year and 10 other fieldsHigh correlation
Prevention of atrocities (POA) Act is highly correlated with Year and 10 other fieldsHigh correlation
Protection of Civil Rights (PCR) Act is highly correlated with Year and 10 other fieldsHigh correlation
Other Crimes Against SCs is highly correlated with Year and 10 other fieldsHigh correlation
TotalCrime is highly correlated with Year and 10 other fieldsHigh correlation
Murder is highly correlated with Assault on women and 9 other fieldsHigh correlation
Assault on women is highly correlated with Murder and 8 other fieldsHigh correlation
Kidnapping and Abduction is highly correlated with Murder and 8 other fieldsHigh correlation
Dacoity is highly correlated with Murder and 8 other fieldsHigh correlation
Robbery is highly correlated with Murder and 8 other fieldsHigh correlation
Arson is highly correlated with Murder and 8 other fieldsHigh correlation
Hurt is highly correlated with Murder and 8 other fieldsHigh correlation
Prevention of atrocities (POA) Act is highly correlated with Murder and 9 other fieldsHigh correlation
Protection of Civil Rights (PCR) Act is highly correlated with Murder and 3 other fieldsHigh correlation
Other Crimes Against SCs is highly correlated with Murder and 9 other fieldsHigh correlation
TotalCrime is highly correlated with Murder and 9 other fieldsHigh correlation
Murder is highly correlated with Assault on women and 7 other fieldsHigh correlation
Assault on women is highly correlated with Murder and 7 other fieldsHigh correlation
Kidnapping and Abduction is highly correlated with Murder and 8 other fieldsHigh correlation
Dacoity is highly correlated with Kidnapping and Abduction and 2 other fieldsHigh correlation
Robbery is highly correlated with Murder and 8 other fieldsHigh correlation
Arson is highly correlated with Murder and 8 other fieldsHigh correlation
Hurt is highly correlated with Murder and 7 other fieldsHigh correlation
Prevention of atrocities (POA) Act is highly correlated with Murder and 7 other fieldsHigh correlation
Other Crimes Against SCs is highly correlated with Murder and 7 other fieldsHigh correlation
TotalCrime is highly correlated with Murder and 7 other fieldsHigh correlation
Assault on women is highly correlated with TotalCrime and 10 other fieldsHigh correlation
TotalCrime is highly correlated with Assault on women and 10 other fieldsHigh correlation
Other Crimes Against SCs is highly correlated with Assault on women and 10 other fieldsHigh correlation
Kidnapping and Abduction is highly correlated with Assault on women and 10 other fieldsHigh correlation
Murder is highly correlated with Assault on women and 10 other fieldsHigh correlation
Protection of Civil Rights (PCR) Act is highly correlated with Assault on women and 10 other fieldsHigh correlation
Robbery is highly correlated with Assault on women and 10 other fieldsHigh correlation
Hurt is highly correlated with Assault on women and 10 other fieldsHigh correlation
Prevention of atrocities (POA) Act is highly correlated with Assault on women and 10 other fieldsHigh correlation
Year is highly correlated with Assault on women and 10 other fieldsHigh correlation
Arson is highly correlated with Assault on women and 10 other fieldsHigh correlation
Dacoity is highly correlated with Assault on women and 10 other fieldsHigh correlation
Year is highly skewed (γ1 = 20.51828431) Skewed
Assault on women is highly skewed (γ1 = 20.20066131) Skewed
Robbery is highly skewed (γ1 = 20.010262) Skewed
Arson is highly skewed (γ1 = 20.09444272) Skewed
Hurt is highly skewed (γ1 = 20.29124178) Skewed
Prevention of atrocities (POA) Act is highly skewed (γ1 = 20.18581884) Skewed
Other Crimes Against SCs is highly skewed (γ1 = 20.14604382) Skewed
TotalCrime is highly skewed (γ1 = 20.51905362) Skewed
STATE/UT is uniformly distributed Uniform
df_index is an unsupported type, check if it needs cleaning or further analysis Unsupported
Murder has 198 (46.9%) zeros Zeros
Assault on women has 178 (42.2%) zeros Zeros
Kidnapping and Abduction has 215 (50.9%) zeros Zeros
Dacoity has 329 (78.0%) zeros Zeros
Robbery has 278 (65.9%) zeros Zeros
Arson has 252 (59.7%) zeros Zeros
Hurt has 185 (43.8%) zeros Zeros
Prevention of atrocities (POA) Act has 165 (39.1%) zeros Zeros
Protection of Civil Rights (PCR) Act has 281 (66.6%) zeros Zeros
Other Crimes Against SCs has 163 (38.6%) zeros Zeros

Reproduction

Analysis started2021-09-27 03:29:20.025439
Analysis finished2021-09-27 03:29:44.353810
Duration24.33 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Unsupported

REJECTED
UNSUPPORTED

Missing0
Missing (%)0.0%
Memory size3.4 KiB

Year
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct13
Distinct (%)3.1%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4003.467933
Minimum2001
Maximum842730
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:44.432796image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum2001
5-th percentile2001
Q12004
median2007
Q32010
95-th percentile2012
Maximum842730
Range840729
Interquartile range (IQR)6

Descriptive statistics

Standard deviation40974.3564
Coefficient of variation (CV)10.23471577
Kurtosis420.999994
Mean4003.467933
Median Absolute Deviation (MAD)3
Skewness20.51828431
Sum1685460
Variance1678897882
MonotonicityNot monotonic
2021-09-27T08:59:44.524798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
201035
8.3%
200235
8.3%
200835
8.3%
200535
8.3%
200435
8.3%
200135
8.3%
200335
8.3%
201135
8.3%
200635
8.3%
201235
8.3%
Other values (3)71
16.8%
ValueCountFrequency (%)
200135
8.3%
200235
8.3%
200335
8.3%
200435
8.3%
200535
8.3%
200635
8.3%
200735
8.3%
200835
8.3%
200935
8.3%
201035
8.3%
ValueCountFrequency (%)
8427301
 
0.2%
201235
8.3%
201135
8.3%
201035
8.3%
200935
8.3%
200835
8.3%
200735
8.3%
200635
8.3%
200535
8.3%
200435
8.3%

STATE/UT
Categorical

UNIFORM

Distinct35
Distinct (%)8.3%
Missing2
Missing (%)0.5%
Memory size3.4 KiB
NAGALAND
 
12
HARYANA
 
12
MEGHALAYA
 
12
MIZORAM
 
12
DAMAN & DIU
 
12
Other values (30)
360 

Length

Max length17
Median length9
Mean length9.485714286
Min length3

Characters and Unicode

Total characters3984
Distinct characters25
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowANDHRA PRADESH
2nd rowANDHRA PRADESH
3rd rowANDHRA PRADESH
4th rowANDHRA PRADESH
5th rowANDHRA PRADESH

Common Values

ValueCountFrequency (%)
NAGALAND12
 
2.8%
HARYANA12
 
2.8%
MEGHALAYA12
 
2.8%
MIZORAM12
 
2.8%
DAMAN & DIU12
 
2.8%
TRIPURA12
 
2.8%
LAKSHADWEEP12
 
2.8%
UTTAR PRADESH12
 
2.8%
GUJARAT12
 
2.8%
KERALA12
 
2.8%
Other values (25)300
71.1%

Length

2021-09-27T08:59:44.870867image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pradesh60
 
9.6%
48
 
7.7%
n24
 
3.8%
uttarakhand12
 
1.9%
rajasthan12
 
1.9%
assam12
 
1.9%
kerala12
 
1.9%
islands12
 
1.9%
jharkhand12
 
1.9%
manipur12
 
1.9%
Other values (34)408
65.4%

Most occurring characters

ValueCountFrequency (%)
A828
20.8%
H360
 
9.0%
R324
 
8.1%
D240
 
6.0%
N216
 
5.4%
204
 
5.1%
S204
 
5.1%
I192
 
4.8%
E168
 
4.2%
M168
 
4.2%
Other values (15)1080
27.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter3732
93.7%
Space Separator204
 
5.1%
Other Punctuation48
 
1.2%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A828
22.2%
H360
9.6%
R324
 
8.7%
D240
 
6.4%
N216
 
5.8%
S204
 
5.5%
I192
 
5.1%
E168
 
4.5%
M168
 
4.5%
T156
 
4.2%
Other values (13)876
23.5%
Space Separator
ValueCountFrequency (%)
204
100.0%
Other Punctuation
ValueCountFrequency (%)
&48
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin3732
93.7%
Common252
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
A828
22.2%
H360
9.6%
R324
 
8.7%
D240
 
6.4%
N216
 
5.8%
S204
 
5.5%
I192
 
5.1%
E168
 
4.5%
M168
 
4.5%
T156
 
4.2%
Other values (13)876
23.5%
Common
ValueCountFrequency (%)
204
81.0%
&48
 
19.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII3984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A828
20.8%
H360
 
9.0%
R324
 
8.1%
D240
 
6.0%
N216
 
5.4%
204
 
5.1%
S204
 
5.1%
I192
 
4.8%
E168
 
4.2%
M168
 
4.2%
Other values (15)1080
27.1%

Murder
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct78
Distinct (%)18.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean37.52969121
Minimum0
Maximum7900
Zeros198
Zeros (%)46.9%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:45.132547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q314
95-th percentile83
Maximum7900
Range7900
Interquartile range (IQR)14

Descriptive statistics

Standard deviation387.6744058
Coefficient of variation (CV)10.32980537
Kurtosis405.6090393
Mean37.52969121
Median Absolute Deviation (MAD)1
Skewness19.96646559
Sum15800
Variance150291.4449
MonotonicityNot monotonic
2021-09-27T08:59:45.424585image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0198
46.9%
122
 
5.2%
317
 
4.0%
211
 
2.6%
49
 
2.1%
59
 
2.1%
117
 
1.7%
127
 
1.7%
76
 
1.4%
136
 
1.4%
Other values (68)129
30.6%
ValueCountFrequency (%)
0198
46.9%
122
 
5.2%
211
 
2.6%
317
 
4.0%
49
 
2.1%
59
 
2.1%
65
 
1.2%
76
 
1.4%
84
 
0.9%
96
 
1.4%
ValueCountFrequency (%)
79001
0.2%
4231
0.2%
3711
0.2%
3231
0.2%
3211
0.2%
3181
0.2%
3101
0.2%
2881
0.2%
2861
0.2%
2391
0.2%

Assault on women
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct113
Distinct (%)26.8%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean75.6152019
Minimum0
Maximum15917
Zeros178
Zeros (%)42.2%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:45.684581image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q335
95-th percentile258
Maximum15917
Range15917
Interquartile range (IQR)35

Descriptive statistics

Standard deviation777.9565229
Coefficient of variation (CV)10.28836138
Kurtosis412.2259878
Mean75.6152019
Median Absolute Deviation (MAD)3
Skewness20.20066131
Sum31834
Variance605216.3516
MonotonicityNot monotonic
2021-09-27T08:59:45.858871image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0178
42.2%
117
 
4.0%
29
 
2.1%
38
 
1.9%
118
 
1.9%
67
 
1.7%
97
 
1.7%
56
 
1.4%
75
 
1.2%
85
 
1.2%
Other values (103)171
40.5%
ValueCountFrequency (%)
0178
42.2%
117
 
4.0%
29
 
2.1%
38
 
1.9%
44
 
0.9%
56
 
1.4%
67
 
1.7%
75
 
1.2%
85
 
1.2%
97
 
1.7%
ValueCountFrequency (%)
159171
0.2%
4122
0.5%
3971
0.2%
3751
0.2%
3671
0.2%
3571
0.2%
3491
0.2%
3431
0.2%
3401
0.2%
3352
0.5%

Kidnapping and Abduction
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct56
Distinct (%)13.3%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean22.22327791
Minimum0
Maximum4678
Zeros215
Zeros (%)50.9%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:46.036094image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q38
95-th percentile36
Maximum4678
Range4678
Interquartile range (IQR)8

Descriptive statistics

Standard deviation230.1116138
Coefficient of variation (CV)10.35453072
Kurtosis401.7343008
Mean22.22327791
Median Absolute Deviation (MAD)0
Skewness19.83284676
Sum9356
Variance52951.35479
MonotonicityNot monotonic
2021-09-27T08:59:46.192220image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0215
50.9%
131
 
7.3%
224
 
5.7%
312
 
2.8%
411
 
2.6%
58
 
1.9%
188
 
1.9%
87
 
1.7%
67
 
1.7%
276
 
1.4%
Other values (46)92
21.8%
ValueCountFrequency (%)
0215
50.9%
131
 
7.3%
224
 
5.7%
312
 
2.8%
411
 
2.6%
58
 
1.9%
67
 
1.7%
76
 
1.4%
87
 
1.7%
93
 
0.7%
ValueCountFrequency (%)
46781
0.2%
3631
0.2%
2581
0.2%
2541
0.2%
2481
0.2%
2192
0.5%
1531
0.2%
1301
0.2%
1131
0.2%
991
0.2%

Dacoity
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct16
Distinct (%)3.8%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean1.876484561
Minimum0
Maximum395
Zeros329
Zeros (%)78.0%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:46.327268image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6
Maximum395
Range395
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.39425401
Coefficient of variation (CV)10.33541891
Kurtosis404.722246
Mean1.876484561
Median Absolute Deviation (MAD)0
Skewness19.93360003
Sum790
Variance376.1370886
MonotonicityNot monotonic
2021-09-27T08:59:46.431228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0329
78.0%
126
 
6.2%
315
 
3.6%
212
 
2.8%
49
 
2.1%
57
 
1.7%
77
 
1.7%
83
 
0.7%
163
 
0.7%
63
 
0.7%
Other values (6)7
 
1.7%
ValueCountFrequency (%)
0329
78.0%
126
 
6.2%
212
 
2.8%
315
 
3.6%
49
 
2.1%
57
 
1.7%
63
 
0.7%
77
 
1.7%
83
 
0.7%
91
 
0.2%
ValueCountFrequency (%)
3951
 
0.2%
221
 
0.2%
201
 
0.2%
171
 
0.2%
163
0.7%
112
 
0.5%
91
 
0.2%
83
0.7%
77
1.7%
63
0.7%

Robbery
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct25
Distinct (%)5.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.527315914
Minimum0
Maximum953
Zeros278
Zeros (%)65.9%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:46.548227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile13
Maximum953
Range953
Interquartile range (IQR)1

Descriptive statistics

Standard deviation46.73468519
Coefficient of variation (CV)10.32282396
Kurtosis406.7279493
Mean4.527315914
Median Absolute Deviation (MAD)0
Skewness20.010262
Sum1906
Variance2184.1308
MonotonicityNot monotonic
2021-09-27T08:59:46.660274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0278
65.9%
140
 
9.5%
317
 
4.0%
411
 
2.6%
210
 
2.4%
69
 
2.1%
57
 
1.7%
115
 
1.2%
105
 
1.2%
85
 
1.2%
Other values (15)34
 
8.1%
ValueCountFrequency (%)
0278
65.9%
140
 
9.5%
210
 
2.4%
317
 
4.0%
411
 
2.6%
57
 
1.7%
69
 
2.1%
75
 
1.2%
85
 
1.2%
94
 
0.9%
ValueCountFrequency (%)
9531
 
0.2%
831
 
0.2%
371
 
0.2%
242
0.5%
221
 
0.2%
202
0.5%
193
0.7%
174
0.9%
161
 
0.2%
152
0.5%

Arson
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct50
Distinct (%)11.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean12.90736342
Minimum0
Maximum2717
Zeros252
Zeros (%)59.7%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:46.777226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35
95-th percentile39
Maximum2717
Range2717
Interquartile range (IQR)5

Descriptive statistics

Standard deviation133.0391581
Coefficient of variation (CV)10.30722958
Kurtosis409.1996997
Mean12.90736342
Median Absolute Deviation (MAD)0
Skewness20.09444272
Sum5434
Variance17699.41759
MonotonicityNot monotonic
2021-09-27T08:59:46.931255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0252
59.7%
125
 
5.9%
217
 
4.0%
410
 
2.4%
510
 
2.4%
38
 
1.9%
77
 
1.7%
126
 
1.4%
106
 
1.4%
135
 
1.2%
Other values (40)75
 
17.8%
ValueCountFrequency (%)
0252
59.7%
125
 
5.9%
217
 
4.0%
38
 
1.9%
410
 
2.4%
510
 
2.4%
63
 
0.7%
77
 
1.7%
85
 
1.2%
94
 
0.9%
ValueCountFrequency (%)
27171
0.2%
1781
0.2%
1031
0.2%
761
0.2%
661
0.2%
621
0.2%
611
0.2%
571
0.2%
532
0.5%
511
0.2%

Hurt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct172
Distinct (%)40.9%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean233.5106888
Minimum0
Maximum49154
Zeros185
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:47.061228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3149
95-th percentile626
Maximum49154
Range49154
Interquartile range (IQR)149

Descriptive statistics

Standard deviation2398.808699
Coefficient of variation (CV)10.27280041
Kurtosis414.7471934
Mean233.5106888
Median Absolute Deviation (MAD)4
Skewness20.29124178
Sum98308
Variance5754283.174
MonotonicityNot monotonic
2021-09-27T08:59:47.197252image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0185
43.8%
113
 
3.1%
47
 
1.7%
27
 
1.7%
35
 
1.2%
485
 
1.2%
94
 
0.9%
74
 
0.9%
233
 
0.7%
123
 
0.7%
Other values (162)185
43.8%
ValueCountFrequency (%)
0185
43.8%
113
 
3.1%
27
 
1.7%
35
 
1.2%
47
 
1.7%
52
 
0.5%
63
 
0.7%
74
 
0.9%
82
 
0.5%
94
 
0.9%
ValueCountFrequency (%)
491541
0.2%
12521
0.2%
9501
0.2%
9001
0.2%
8901
0.2%
8771
0.2%
8581
0.2%
8211
0.2%
8151
0.2%
7221
0.2%

Prevention of atrocities (POA) Act
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct191
Distinct (%)45.4%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean591.7244656
Minimum0
Maximum124558
Zeros165
Zeros (%)39.1%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:47.346230image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q3267
95-th percentile1514
Maximum124558
Range124558
Interquartile range (IQR)267

Descriptive statistics

Standard deviation6089.419785
Coefficient of variation (CV)10.29097179
Kurtosis411.8054618
Mean591.7244656
Median Absolute Deviation (MAD)8
Skewness20.18581884
Sum249116
Variance37081033.31
MonotonicityNot monotonic
2021-09-27T08:59:47.529228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0165
39.1%
126
 
6.2%
210
 
2.4%
44
 
0.9%
364
 
0.9%
243
 
0.7%
533
 
0.7%
1502
 
0.5%
212
 
0.5%
322
 
0.5%
Other values (181)200
47.4%
ValueCountFrequency (%)
0165
39.1%
126
 
6.2%
210
 
2.4%
32
 
0.5%
44
 
0.9%
52
 
0.5%
82
 
0.5%
101
 
0.2%
121
 
0.2%
132
 
0.5%
ValueCountFrequency (%)
1245581
0.2%
48851
0.2%
44361
0.2%
30721
0.2%
30241
0.2%
29741
0.2%
29651
0.2%
25541
0.2%
25481
0.2%
25341
0.2%

Protection of Civil Rights (PCR) Act
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct57
Distinct (%)13.5%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean20.28503563
Minimum0
Maximum4270
Zeros281
Zeros (%)66.6%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:47.697227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile68
Maximum4270
Range4270
Interquartile range (IQR)2

Descriptive statistics

Standard deviation210.6970619
Coefficient of variation (CV)10.38682237
Kurtosis396.759567
Mean20.28503563
Median Absolute Deviation (MAD)0
Skewness19.66194537
Sum8540
Variance44393.25189
MonotonicityNot monotonic
2021-09-27T08:59:47.875226image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0281
66.6%
130
 
7.1%
212
 
2.8%
311
 
2.6%
125
 
1.2%
104
 
0.9%
54
 
0.9%
204
 
0.9%
43
 
0.7%
263
 
0.7%
Other values (47)64
 
15.2%
ValueCountFrequency (%)
0281
66.6%
130
 
7.1%
212
 
2.8%
311
 
2.6%
43
 
0.7%
54
 
0.9%
63
 
0.7%
71
 
0.2%
83
 
0.7%
92
 
0.5%
ValueCountFrequency (%)
42701
0.2%
4591
0.2%
3121
0.2%
1981
0.2%
1651
0.2%
1331
0.2%
1231
0.2%
1222
0.5%
1131
0.2%
1011
0.2%

Other Crimes Against SCs
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct186
Distinct (%)44.2%
Missing1
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean758.631829
Minimum0
Maximum159692
Zeros163
Zeros (%)38.6%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:48.057244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q3283
95-th percentile2645
Maximum159692
Range159692
Interquartile range (IQR)283

Descriptive statistics

Standard deviation7812.209512
Coefficient of variation (CV)10.29776133
Kurtosis410.7122497
Mean758.631829
Median Absolute Deviation (MAD)6
Skewness20.14604382
Sum319384
Variance61030617.45
MonotonicityNot monotonic
2021-09-27T08:59:48.230228image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0163
38.6%
122
 
5.2%
210
 
2.4%
36
 
1.4%
45
 
1.2%
64
 
0.9%
313
 
0.7%
223
 
0.7%
133
 
0.7%
1273
 
0.7%
Other values (176)199
47.2%
ValueCountFrequency (%)
0163
38.6%
122
 
5.2%
210
 
2.4%
36
 
1.4%
45
 
1.2%
52
 
0.5%
64
 
0.9%
82
 
0.5%
92
 
0.5%
101
 
0.2%
ValueCountFrequency (%)
1596921
0.2%
47711
0.2%
45361
0.2%
42961
0.2%
42391
0.2%
40141
0.2%
39741
0.2%
37951
0.2%
36831
0.2%
36761
0.2%

TotalCrime
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct238
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22994.5782
Minimum0
Maximum4851856
Zeros1
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size3.4 KiB
2021-09-27T08:59:48.417479image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8012
Q18036
median8212
Q312604
95-th percentile25600.4
Maximum4851856
Range4851856
Interquartile range (IQR)4568

Descriptive statistics

Standard deviation235713.6572
Coefficient of variation (CV)10.25083631
Kurtosis421.351172
Mean22994.5782
Median Absolute Deviation (MAD)202
Skewness20.51905362
Sum9703712
Variance5.556092819 × 1010
MonotonicityNot monotonic
2021-09-27T08:59:48.588458image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
802818
 
4.3%
803218
 
4.3%
804416
 
3.8%
804815
 
3.6%
804015
 
3.6%
801614
 
3.3%
801212
 
2.8%
800812
 
2.8%
803610
 
2.4%
80209
 
2.1%
Other values (228)283
67.1%
ValueCountFrequency (%)
01
 
0.2%
80047
 
1.7%
800812
2.8%
801212
2.8%
801614
3.3%
80209
2.1%
80248
1.9%
802818
4.3%
803218
4.3%
803610
2.4%
ValueCountFrequency (%)
48518561
0.2%
509321
0.2%
400681
0.2%
397161
0.2%
388521
0.2%
381241
0.2%
368761
0.2%
331281
0.2%
328561
0.2%
326041
0.2%

Interactions

2021-09-27T08:59:26.191682image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:26.293733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:26.407733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:26.514735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:26.615736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:26.721731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:26.815736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:26.913652image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.008648image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.123675image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.219186image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.325270image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.413282image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.512284image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.668726image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.837819image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:27.989807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.108807image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.226217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.339208image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.451207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.577214image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.683207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.814213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:28.924213image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.030215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.154212image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.288605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.402042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.530154image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.644140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.756163image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:29.914274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.044187image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.168420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.290420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.404421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.493421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.612200image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.723194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.815192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:30.918192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.010194image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.109719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.209721image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.311719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.406720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.513720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.612739image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.737401image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:31.860396image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:32.010956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:32.152984image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:32.273956image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:32.811042image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:32.929041image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.048044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.173050image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.293736image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.427074image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.541070image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.634064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.761075image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.873086image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:33.967982image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.067983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.177495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.289398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.389398image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.490422image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.584399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.695078image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.792120image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.887129image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:34.992125image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.097347image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.208352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.338346image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.427438image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.519435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.615435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.722348image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.823442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:35.931353image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.030362image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.128435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.235435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.350883image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.455886image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.554882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.646155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.743140image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.836135image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:36.934058image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.031164image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.147155image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.247064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.352064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.481663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.621731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.742733image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.864642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:37.964642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.064642image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.170719image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.282725image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.383731image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.509978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.631063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.726071image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.848978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:38.964978image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.064974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.177974image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.284972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.382973image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.487009image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.598007image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.698006image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.812577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:39.911663image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:40.038583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:40.194665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:40.339656image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:40.466661image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:40.653665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:40.827665image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:40.941577image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.052657image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.172612image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.292775image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.425694image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.549693image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.647799image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.757793image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.879780image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:41.985784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:42.101696image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:42.204798image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:42.307772image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:42.410811image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:42.526818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:42.635818image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-09-27T08:59:42.756805image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-09-27T08:59:48.829312image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-27T08:59:49.136352image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-27T08:59:49.444364image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-27T08:59:49.794255image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-09-27T08:59:43.021806image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-09-27T08:59:43.337605image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2021-09-27T08:59:43.919699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2021-09-27T08:59:44.232882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexYearSTATE/UTMurderAssault on womenKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCsTotalCrime
002001.0ANDHRA PRADESH45.069.022.03.02.06.0518.0950.0312.01006.019736.0
112002.0ANDHRA PRADESH60.098.018.00.04.012.0568.0830.0459.01336.021548.0
222003.0ANDHRA PRADESH33.079.027.01.015.04.0615.01234.0165.01386.022248.0
332004.0ANDHRA PRADESH39.066.028.00.07.020.0474.01319.068.01234.021036.0
442005.0ANDHRA PRADESH37.074.021.00.00.09.0459.01244.061.01212.020488.0
552006.0ANDHRA PRADESH52.097.012.03.05.013.0657.01514.093.01445.023588.0
662007.0ANDHRA PRADESH46.0105.025.00.00.017.0541.01200.0122.01327.021560.0
772008.0ANDHRA PRADESH48.088.018.00.00.05.0651.01383.0123.01682.024024.0
882009.0ANDHRA PRADESH35.099.019.01.04.012.0722.01737.039.01836.026052.0
992010.0ANDHRA PRADESH43.0100.018.00.01.017.0709.01509.050.01874.025324.0

Last rows

df_indexYearSTATE/UTMurderAssault on womenKidnapping and AbductionDacoityRobberyArsonHurtPrevention of atrocities (POA) ActProtection of Civil Rights (PCR) ActOther Crimes Against SCsTotalCrime
4124122005.0PUDUCHERRY0.00.00.00.00.00.00.02.012.00.08076.0
4134132006.0PUDUCHERRY0.00.00.00.00.00.00.00.014.00.08080.0
4144142007.0PUDUCHERRY1.00.00.00.00.00.00.00.024.00.08128.0
4154152008.0PUDUCHERRY0.00.00.00.00.00.00.02.027.00.08148.0
4164162009.0PUDUCHERRY0.00.00.00.00.00.00.03.026.00.08152.0
4174172010.0PUDUCHERRY1.00.01.00.00.00.01.02.026.00.08164.0
4184182011.0PUDUCHERRY0.00.00.00.00.00.00.01.015.02.08116.0
4194192012.0PUDUCHERRY2.00.00.00.00.00.00.01.020.01.08144.0
420TotalCrimeNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.0
421Column_Total842730.0NaN7900.015917.04678.0395.0953.02717.049154.0124558.04270.0159692.04851856.0